Molecular characterization, DNA fingerprinting and genetic diversity analysis of Nepalese rice landraces using SSR markers
Bibliographic record
Abstract
Rice (Oryza sativa) is the major crop of Nepal. Genetic diversity studies in rice have been conducted extensively with collections from various parts of the world, including Nepal. However, local landraces in these collections are explored on a very limited scale for novel genetic variations. The availability of wild relatives of Oryza sativa has increased interest in understanding the genetic makeup of Nepalese rice landraces. This study aimed to identify the variability in 80 rice landraces using 19 simple sequence repeat (SSR) markers. The collection represented geographical regions suitable for rice farming in Nepal. DNA fingerprints of some landraces showed clear distinctions. The results indicated significant genetic differentiation among the rice landraces, with a possible formation of two distinct clusters. Among 19 SSR markers, only 12 have shown polymorphism. The lowest allele frequency was observed in the Tauli Satara landrace. The maximum heterozygosity was observed from the sample collected from Pyuthan district. The first coordinate explained 18.81% of the variation, while the second coordinate explained 13.16%. Overall, these findings will benefit rice breeders and conservationists in selecting parent material, managing conservation efforts both on-farm and ex-situ, and linking genetic diversity with geographical locations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".